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\begin{document}
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\title{Optimal Scheduling of Air Conditioning for Renewable Energy
Accommodation}



\author[1]{Xiang Zhang}%
\author[2]{Li Zhong}%
\author[2]{Chencheng Wang}%
\author[2]{Linhuan Li}%
\author[2]{Jiefu Zhang}%
\author[3]{Zhou Han}%
\affil[1]{State Grid Sichuan Electric Power Company Marketing Service Center}%
\affil[2]{ State Grid Sichuan Electric Power Company Marketing Service Center}%
\affil[3]{Anhui Nanrui Zhongtian Power Electronics Co., Ltd.}%


\vspace{-1em}



  
  \date{April 25, 2026}


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\begin{abstract}
The rapid integration of renewable energy generation has reduced the
flexibility regulation capacity of power systems, necessitating the
exploration of adjustable resources on the load side to establish a
novel ''source-load interaction'' balancing mechanism. Air conditioning
(AC) load, as a critical demand response resource, has garnered
increasing attention. However, existing AC load control strategies are
either heavily influenced by user behavior uncertainty or overly reliant
on communication and measurement infrastructure. Moreover, most
approaches adopt random switching control methods, which fail to
maximize user participation willingness and overlook the dynamic
variations in user responsiveness, ultimately limiting their practical
effectiveness. To address these challenges, this study proposes a
dynamic scheduling model that comprehensively considers the aggregated
response potential of AC loads and the characteristics of multiple
flexible load types, thereby fully exploiting the coordinated regulation
capability of load-side resources. Targeting a low-carbon community
scenario (incorporating distributed wind power and residential users),
the model is formulated with the dual objectives of maximizing wind
power accommodation and minimizing source-load power deviation. A greedy
algorithm is employed to iteratively solve the maximum available
response capacity and actual dispatchable quantity of AC loads in each
time slot, enabling dynamic updates of potential assessment and
scheduling decisions.%
\end{abstract}%



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\verb`Optimal Scheduling of Air Conditioning for Renewable Energy Accommodation.docx`  available at \url{https://authorea.com/users/924161/articles/1311060-optimal-scheduling-of-air-conditioning-for-renewable-energy-accommodation}

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